Professor Bolin earns American Statistical Association Section on Statistics and the Environment Early Investigator Award

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By David Murphy

KAUST Associate Professor of Mathematical Statistics David Bolin has been selected for the American Statistical Association Section on Statistics and the Environment Early Investigator (ENVR) Award for his outstanding contributions to environmental statistics.

The ENVR Awards recognize outstanding contributions from a highly competitive pool of nominees to the development of methods, issues, concepts, applications, and initiatives in environmental statistics.

The award citation noted the impact of Bolin’s research and granted the award "for his contribution in fundamental statistical theory and methodology, in particular, stochastic partial differential equations and their applications in statistics, with a focus on the development of practical, computationally efficient tools for modeling non-stationary and non-Gaussian processes for applications in the environmental sciences."

ENVR will honor Bolin and his fellow award winners at its annual Business Meeting and Awards Ceremony during the 2022 Joint Statistical Meetings held in Washington D.C., U.S., from August 6 to 11. Of his election, Professor Bolin said, “I am honored to receive the award and happy that the methods we are developing in my group are being recognized as important for the environmental sciences. I am also very grateful to my colleagues and collaborators for the support that made this possible.”

Bolin joined the KAUST Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division in November 2019 from the Department of Mathematical Sciences at the University of Gothenburg, where he held the position of associate professor of mathematical statistics.

Before joining the University, he received his Ph.D. degree ('12) in mathematical statistics and M.Sc. in engineering mathematics ('07) from Lund University, Sweden, in 2012 and 2007, respectively.

Bolin’s primary research areas are spatial statistics, spatio-temporal modeling, and computationally efficient inference methods to analyze large data sets. He is particularly intrigued by mathematical and statistical problems that are motivated by real needs in the applied sciences.

Outside of his day-to-day research at KAUST, the Swedish researcher leads the Stochastic Processes and Applied Statistics (StochProc) research group. The StochProc team focuses on statistical methodology for stochastic processes and random fields based on stochastic partial differential equations.

“Most of the research projects that we work on in the StochProc group span all the way from mathematical theory to software implementations and concrete applications in areas such as environmental sciences. “Easy-to-use software has enabled scientists in many application areas to use the methods that we have developed. We aim to continue this mix of theoretical and applied research to develop theoretically justified methods that can have a tangible impact in the applied sciences,” he concluded.